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Samsung

Industry researchasia · kr
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Research library566linked papers
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Selected work

Representative Papers

GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models

Jun 07, 2024arXiv.org

Existing no-reference image quality assessment (IQA) methods exhibit poor cross-dataset generalization, particularly under distribution shifts such as user-generated content, synthetic imagery, and low-light conditions. To address this, we propose the first generic IQA framework leveraging the cross-attention mechanism of text-guided latent diffusion models (LDMs). Our method introduces learnable, quality-aware textual prompts and models prompt–image alignment to derive robust quality representations. Crucially, it exploits intermediate cross-attention features from the LDM denoising process—enabling zero-shot transfer to multiple benchmark datasets without fine-tuning. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods on diverse databases including LIVE-Youtube, KoNViD, and UHD-1. Moreover, it achieves superior out-of-distribution generalization, validating its effectiveness under substantial domain shifts. This work establishes a novel paradigm for leveraging generative model priors in blind IQA, bridging semantic understanding and perceptual quality estimation.

5 citations1 influentialRead paper

Dream to Generalize: Zero-Shot Model-Based Reinforcement Learning for Unseen Visual Distractions

Jun 26, 2023AAAI Conference on Artificial Intelligence

To address the degradation of model-based reinforcement learning (MBRL) generalization under high-dimensional visual observations corrupted by clouds, shadows, and illumination variations, this paper proposes Dr. G—a zero-shot model-based RL framework. Our approach tackles this challenge through three key contributions: (1) a novel dual-contrastive self-supervised learning mechanism that disentangles and encodes task-relevant features from multi-view augmented data; (2) recurrent state-wise inverse dynamics modeling to enhance the world model’s temporal causal understanding; and (3) zero-shot cross-background transfer without fine-tuning. Evaluated on DeepMind Control (with complex video backgrounds) and Robosuite (with randomized environments), Dr. G achieves performance gains of 117% and 14%, respectively, over state-of-the-art methods. The implementation is publicly available.

5 citationsRead paper

Wavelet-Driven Masked Multiscale Reconstruction for PPG Foundation Models

Jan 18, 2026

This work addresses the limitation of existing PPG foundation models, which overlook the multi-band spectral structure of photoplethysmographic signals during pretraining and consequently struggle to effectively capture multiscale physiological features ranging from fine-grained waveforms to global rhythms. To overcome this, we propose a Masked Multi-scale Reconstruction (MMR) framework that, for the first time, integrates wavelet-driven multi-resolution time–frequency representations into self-supervised PPG learning. Specifically, the input signal is decomposed via wavelet transform, and randomly masked wavelet coefficients are reconstructed within a Transformer encoder, thereby explicitly fusing multiscale time–frequency information. Evaluated across 19 health-related tasks, our method matches or surpasses current state-of-the-art open-source PPG and general-purpose time-series foundation models on 17 tasks, significantly enhancing the physiological interpretability, generalization, and robustness of learned representations.

3 citationsRead paper

Improving Equivariant Networks with Probabilistic Symmetry Breaking

Mar 27, 2025

Equivariant networks strictly preserve input symmetries, rendering them ill-suited for generative tasks requiring *active symmetry breaking*—e.g., reconstructing asymmetric structures from highly symmetric latent representations. To address this, we establish the first necessary and sufficient representation theorem for equivariant conditional distributions and propose SymPE: a method that achieves *controllable symmetry breaking* via learnable stochastic normalized positional encodings, while preserving the group-equivariant inductive bias. SymPE unifies probabilistic symmetry breaking, positional encoding, and equivariant graph neural networks, and naturally integrates with diffusion-based generative frameworks. Empirically, it significantly improves performance on graph diffusion modeling, graph autoencoding, and lattice spin system generation. Theoretically, we prove that SymPE’s generalization bound is strictly superior to that of conventional equivariant networks.

3 citationsRead paper

Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images

Jul 23, 20232023 18th International Conference on Machine Vision and Applications (MVA)

In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are hard to spot out in RGB images and by a model trained with only normal data. To address this, we propose a novel contrastive learning scheme for knowledge distillation-based anomaly detection. In our framework, Multiresolution Knowledge Distillation (MKD) is adopted as a baseline, which operates by measuring feature similarities between the teacher and student networks. Based on MKD, we propose a novel contrastive learning method, namely Multiresolution Contrastive Distillation (MCD), which does not require positive/negative pairs with an anchor but operates by pulling/pushing the distance between the teacher and student features. Furthermore, we propose the blending module that transforms and aggregate multi-channel information to the three-channel input layer of MCD. Our proposed method significantly outperforms competitive state-of-the-art methods in both AUROC and accuracy metrics on the collected Multi-illumination and Multi-focus display image dataset for Anomaly Detection (MMdAD).

3 citationsRead paper
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